Biomimetic control systems for robotic arms based on virtual reality

To address the problems of redundant joint movement and low energy efficiency in traditional mechanical arm path planning methods, this study proposes an innovative control scheme that integrates digital twins and computer vision. By building the PICO VR virtual control system and the Isaac Sim physics simulation platform, the bionic motion control of a 6-degree-of-freedom robotic arm was achieved. This scheme breaks through the limitation of the traditional ROS-MoveIt architecture, and adopts the bidirectional data mapping mechanism to establish a virtual and real synchronization system. The operator can directly control the virtual robot arm at the joint level through the VR controller. The experiment shows that the method can reduce the unnecessary joint displacement by 32.6%. Especially in the end effector control, the MediaPipe hand key point recognition algorithm is integrated to build a motion mapping model based on LSTM network to realize 17ms delay real-time imitation of manipulator and human hand movements. The system synchronizes data between virtual environment and physical robot arm through JSON protocol. Verified by the robot arm platform, this scheme can reduce 28.4% path planning energy consumption and improve 43% human-computer interaction efficiency compared with traditional methods. The research results show that this system can effectively solve the problem of motion redundancy in traditional path planning through the virtual and real fusion control strategy, and provide a new technical path for intelligent robot control.